Approximating Probability Densities by Mixtures of Gaussian Dependence Trees

0435901 - ÚTIA 2015 RIV CZ eng C - Conference Paper (international conference)
Grim, Jiří
Approximating Probability Densities by Mixtures of Gaussian Dependence Trees.
Stochastic and Physical Monitoring Systems, SPMS 2014. Praha: ČVUT, 2014. ISBN 978-80-01-05616-5.
[Stochastic and Physical Monitoring Systems SPMS 2014. Malá Skála (CZ), 23.06.2014-28.06.2014]
R&D Projects: GA ČR(CZ) GA14-02652S; GA ČR(CZ) GA14-10911S
Institutional support: RVO:67985556
Keywords : Multivariate statistics * Mixtures of dependence trees * EM algorithm * Pattern recognition * Medical image analysis
Subject RIV: IN - Informatics, Computer Science
http://library.utia.cas.cz/separaty/2014/RO/grim-0435901.pdf

Considering the probabilistic approach to practical problems we are increasingly confronted with the need to estimate unknown multivariate probability density functions from large high-dimensional databases produced by electronic devices. The underlying densities are usually strongly multimodal and therefore mixtures of unimodal density functions suggest themselves as a suitable approximation tool. In this respect the product mixture models are preferable because they can be efficiently estimated from data by means of EM algorithm and have some advantageous properties. However, in some cases the simplicity of product components could appear too restrictive and a natural idea is to use a more complex mixture of dependence-tree densities. The dependence tree densities can explicitly describe the statistical relationships between pairs of variables at the level of individual components and therefore the approximation power of the resulting mixture may essentially increase.
Permanent Link: http://hdl.handle.net/11104/0241872